Variable labels
You can use the {labelled} package to define variable
labels. They will be automatically used by ggcoef_model().
Note that variable labels should be defined before computing the
model.
library(labelled)
tips_labelled <- tips %>%
set_variable_labels(
day = "Day of the week",
time = "Lunch or Dinner",
total_bill = "Bill's total"
)
mod_labelled <- lm(tip ~ day + time + total_bill, data = tips_labelled)
ggcoef_model(mod_labelled)
You can also define custom variable labels directly by passing a
named vector to the variable_labels option.
ggcoef_model(
mod_simple,
variable_labels = c(
day = "Week day",
time = "Time (lunch or dinner ?)",
total_bill = "Total of the bill"
)
)
If variable labels are to long, you can pass
ggplot2::label_wrap_gen() or any other labeller function to
facet_labeller.
ggcoef_model(
mod_simple,
variable_labels = c(
day = "Week day",
time = "Time (lunch or dinner ?)",
total_bill = "Total of the bill"
),
facet_labeller = ggplot2::label_wrap_gen(10)
)
Use facet_row = NULL to hide variable names.
ggcoef_model(mod_simple, facet_row = NULL, colour_guide = TRUE)
Term labels
Several options allows you to customize term labels.
ggcoef_model(mod_titanic, exponentiate = TRUE)
ggcoef_model(
mod_titanic,
exponentiate = TRUE,
show_p_values = FALSE,
signif_stars = FALSE,
add_reference_rows = FALSE,
categorical_terms_pattern = "{level} (ref: {reference_level})",
interaction_sep = " x "
) +
ggplot2::scale_y_discrete(labels = scales::label_wrap(15))
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
By default, for categorical variables using treatment and sum
contrasts, reference rows will be added and displayed on the graph.
mod_titanic2 <- glm(
Survived ~ Sex * Age + Class,
weights = Freq,
data = d_titanic,
family = binomial,
contrasts = list(Sex = contr.sum, Class = contr.treatment(4, base = 3))
)
ggcoef_model(mod_titanic2, exponentiate = TRUE)
Continuous variables with polynomial terms defined with
stats::poly() are also properly managed.
mod_poly <- lm(Sepal.Length ~ poly(Petal.Width, 3) + Petal.Length, data = iris)
ggcoef_model(mod_poly)
Use no_reference_row to indicate which variables should
not have a reference row added.
ggcoef_model(
mod_titanic2,
exponentiate = TRUE,
no_reference_row = "Sex"
)
ggcoef_model(
mod_titanic2,
exponentiate = TRUE,
no_reference_row = broom.helpers::all_dichotomous()
)
ggcoef_model(
mod_titanic2,
exponentiate = TRUE,
no_reference_row = broom.helpers::all_categorical(),
categorical_terms_pattern = "{level}/{reference_level}"
)
Elements to display
Use intercept = TRUE to display intercepts.
ggcoef_model(mod_simple, intercept = TRUE)
You can remove confidence intervals with
conf.int = FALSE.
ggcoef_model(mod_simple, conf.int = FALSE)
By default, significant terms (i.e. with a p-value below 5%) are
highlighted using two types of dots. You can control the level of
significance with significance or remove it with
significance = NULL.
ggcoef_model(mod_simple, significance = NULL)
By default, dots are colored by variable. You can deactivate this
behavior with colour = NULL.
ggcoef_model(mod_simple, colour = NULL)
You can display only a subset of terms with
include.
ggcoef_model(mod_simple, include = c("time", "total_bill"))
It is possible to use tidyselect helpers.
ggcoef_model(mod_simple, include = dplyr::starts_with("t"))
You can remove stripped rows with
stripped_rows = FALSE.
ggcoef_model(mod_simple, stripped_rows = FALSE)
Do not hesitate to consult the help file of
ggcoef_model() to see all available options.
ggplot2 elements
The plot returned by ggcoef_model() is a classic
ggplot2 plot. You can therefore apply ggplot2
functions to it.
ggcoef_model(mod_simple) +
ggplot2::xlab("Coefficients") +
ggplot2::ggtitle("Custom title") +
ggplot2::scale_color_brewer(palette = "Set1") +
ggplot2::theme(legend.position = "right")
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
